Climate Change Policy Exploration using Reinforcement Learning
- URL: http://arxiv.org/abs/2211.17013v1
- Date: Sun, 23 Oct 2022 18:20:17 GMT
- Title: Climate Change Policy Exploration using Reinforcement Learning
- Authors: Theodore Wolf
- Abstract summary: We use four different Reinforcement Learning agents varying in complexity to probe the environment in different ways.
We use a reward function based on planetary boundaries that we modify to force the agents to find a wider range of strategies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate Change is an incredibly complicated problem that humanity faces. When
many variables interact with each other, it can be difficult for humans to
grasp the causes and effects of the very large-scale problem of climate change.
The climate is a dynamical system, where small changes can have considerable
and unpredictable repercussions in the long term. Understanding how to nudge
this system in the right ways could help us find creative solutions to climate
change.
In this research, we combine Deep Reinforcement Learning and a World-Earth
system model to find, and explain, creative strategies to a sustainable future.
This is an extension of the work from Strnad et al. where we extend on the
method and analysis, by taking multiple directions. We use four different
Reinforcement Learning agents varying in complexity to probe the environment in
different ways and to find various strategies. The environment is a
low-complexity World Earth system model where the goal is to reach a future
where all the energy for the economy is produced by renewables by enacting
different policies. We use a reward function based on planetary boundaries that
we modify to force the agents to find a wider range of strategies. To favour
applicability, we slightly modify the environment, by injecting noise and
making it fully observable, to understand the impacts of these factors on the
learning of the agents.
Related papers
- Crafting desirable climate trajectories with RL explored socio-environmental simulations [3.554161433683967]
Integrated Assessment Models (IAMs) combine social, economic, and environmental simulations to forecast potential policy effects.
Recent preliminary work using Reinforcement Learning (RL) to replace the traditional solvers shows promising results in decision making in uncertain and noisy scenarios.
We extend on this work by introducing multiple interacting RL agents as a preliminary analysis on modelling the complex interplay of socio-interactions between various stakeholders or nations.
arXiv Detail & Related papers (2024-10-09T13:21:50Z) - HAZARD Challenge: Embodied Decision Making in Dynamically Changing
Environments [93.94020724735199]
HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind.
This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
arXiv Detail & Related papers (2024-01-23T18:59:43Z) - Analyzing Regional Impacts of Climate Change using Natural Language
Processing Techniques [0.9387233631570752]
We use BERT (Bidirectional Representations from Transformers) for Named Entity Recognition (NER) to identify specific geographies within the climate literature.
We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area.
These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies.
arXiv Detail & Related papers (2024-01-11T16:44:59Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - The Introspective Agent: Interdependence of Strategy, Physiology, and
Sensing for Embodied Agents [51.94554095091305]
We argue for an introspective agent, which considers its own abilities in the context of its environment.
Just as in nature, we hope to reframe strategy as one tool, among many, to succeed in an environment.
arXiv Detail & Related papers (2022-01-02T20:14:01Z) - ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods [89.61670857155173]
We present our solution to simulate photo-realistic floods on authentic images.
We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
arXiv Detail & Related papers (2021-10-06T15:54:57Z) - Remote sensing, AI and innovative prediction methods for adapting cities
to the impacts of the climate change [0.0]
I propose an AI-based framework which might be useful for extracting indicators from remote sensing images.
I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods.
arXiv Detail & Related papers (2021-07-06T15:55:26Z) - Quantum technologies for climate change: Preliminary assessment [0.0]
Climate change presents an existential threat to human societies and the Earth's ecosystems.
Quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change.
This report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas.
arXiv Detail & Related papers (2021-06-23T18:02:19Z) - Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context.
This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG)
We present this tool and the methodology that we used to develop it in the present article.
arXiv Detail & Related papers (2020-11-03T21:22:42Z) - Ecological Reinforcement Learning [76.9893572776141]
We study the kinds of environment properties that can make learning under such conditions easier.
understanding how properties of the environment impact the performance of reinforcement learning agents can help us to structure our tasks in ways that make learning tractable.
arXiv Detail & Related papers (2020-06-22T17:55:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.